Objectives
Data Analysis is described as the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision- making. Performing such tasks over large and heterogeneous collections of tabular data, as found in enterprise data lakes and on the Web, is extremely challenging and an attractive research topic in data management, AI, and related communities. The goal of this workshop is to bring together researchers and practitioners in these diverse communities that work on addressing the fundamental research challenges of tabular data analysis and building automated solutions in this space
We aim to provide a forum for: a) exchange of ideas between two communities: 1) an active community of data management researchers working on data integration and schema and data matching problems over tabular data, and 2) a vibrant community of researchers in AI and Semantic Web communities working on the core challenge of matching tabular data to Knowledge Graphs as a part of the ISWC SemTab Challenges. b) presentation of late-breaking results related to several emerging research areas such as table representation learning and its applications, automation of data science pipelines, and data lake and data lakehouse solutions. c) discussion of real-world data management challenges related to implementing industrial scale tabular data anaylsis solutions.